Expert thinking on productivity, intangible asset valuation, growth strategy, and building more valuable businesses.
Pharmaceuticals were among the first sectors to pioneer IP-backed financing through patent royalty securitisations. But the opportunity extends far beyond royalty streams. Drug patents, clinical data packages, regulatory approvals, and formulation know-how constitute a rich intangible asset base that most PE-backed pharma companies underexploit as collateral.
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When 92% of S&P 500 value is attributable to intangible assets but only 8% appears on balance sheets, there is a fundamental disconnect between reported financial position and economic reality. This thought experiment reconstructs a typical SaaS company's balance sheet as if intangibles were properly captured. The results are illuminating.
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Data is now formally recognised as productive capital by the System of National Accounts. But how do you value it on a balance sheet? Tony Hillier draws on 30 years of structured finance experience to map three valuation approaches — and where data assets fit into modern capital structures.
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National accounts now recognise R&D and software as capital assets. But the majority of intangible investment — organizational know-how, proprietary data, trained workforces, customer networks — still falls outside the measurement boundary. These are the seven categories that distort our understanding of productivity.
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Under current accounting standards, most AI development costs are expensed immediately — treated as period costs rather than capital investments. Yet SNA 2025 treats data as productive capital, and FASB's ASU 2025-06 changes when US companies start capitalising software. This creates a widening gap between economic reality and financial reporting. Here is how to navigate it.
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The intangible asset gap costs growth-stage companies millions in undervaluation. Learn how to identify and close it before your next fundraise.
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Technology and SaaS companies sit on vast portfolios of intangible assets — proprietary code, patents, customer contracts, data — yet most still rely on equity dilution or unsecured debt to fund growth. Structured lending against these assets offers a capital-efficient alternative that PE firms and fund managers are only beginning to exploit.
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The frontier of structured finance is lending against the intangible assets that constitute AI company value: proprietary models, training datasets, and customer contracts. The question is no longer whether this is possible, but how to structure it and price the risk appropriately.
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PE operating partners managing 5-15 portfolio companies face a new dimension of value creation: assessing which portfolio companies are building genuine AI intangible assets and which are pursuing fashionable but value-destructive AI-washing. Here is the assessment framework that separates signal from noise.
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Traditional productivity metrics were designed for an economy dominated by physical capital and tangible output. In a world where the most valuable firms derive their competitive advantage from software, data, brand equity, and organizational know-how, those metrics are increasingly unreliable. Here is why the gap matters — and what we should do about it.
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The San Francisco Fed's recent research on AI and productivity is among the most rigorous work in the space. But it also reveals critical blindspots in how economists measure the impact of AI on economic growth. Here is what they got right, what they missed, and why the measurement gap itself is the most important finding.
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Board oversight of AI investment is becoming as critical as financial controls. Here is the 10-point accountability checklist that separates effective board governance from box-ticking.
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